The Instability of Family Earnings and Family Income in Canada, 1986 to 1991 and 1996 to 2001
Bibliographic record
Abstract
We investigate how family earnings instability evolved between the late 1980s and the late 1990s and how family income instability varies across segments of the (family-level) earnings distribution. We uncover four key patterns. First, among the subset of families who were intact over the 198291 and 19922001 periods, family earnings instability changed little between the late 1980s and the late 1990s. Second, the dispersion of families' permanent earnings became much more unequal during that period. Third, families who were in the bottom tertile of the (age-specific) earnings distribution in 1992 to 1995 had, during the 19962001 period, much more unstable market income than their counterparts in the top tertile. Fourth, among families with husbands aged under 45, the tax and transfer system, during the 19962001 period, eliminated at least two-thirds (and up to all) of the differences in instability (measured in terms of proportional income gains/losses) in family market income that were observed during that period between families in the bottom tertile and those in the top tertile. This finding highlights the key stabilization role played by the tax and transfer system, a feature that received relatively little attention during the 1990s when (UI) EI and Social Assistance were reformed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".